Keynote Speakers

Abstract: In this talk I will introduce Learning Engines, which provide an integrated ecosystem for understanding, planing and controlling complex dynamical systems and networks ranging from healthcare systems to smart cities. Learning Engines use cutting-edge machine learning, AI and operations research theory, methods, algorithms and systems to extract timely insights from multi-modal data acquired from the operation of these systems over time and subsequently provide actionable intelligence to a variety of stakeholders involved in their operation and control. Key technologies which we have developed in order to fuel the Learning Engines, and which will be highlighted in this talk, are new automated machine learning, causal inference and reinforcement learning methods.

Bio: Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Faculty Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalized medicine. Prior to this, she was a Chancellor's Professor at UCLA and MAN Professor of Quantitative Finance at University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 33 granted USA patents. Her current research focus is on data science, artificial intelligence and machine learning for medicine and education.

Abstract: In this talk, I will describe the next generation of emerging WLAN devices that will take us from millimeter wave bands up to terahertz. I will describe future WLAN capabilities in high-frequency bands spanning sensing, security, and data rate. Moreover, I will describe new application drivers that can exploit these features. Lastly, I will describe challenges  in control plane design and how to efficiently harness the vast capabilities of emerging systems. Throughout, I will draw on results from early prototypes and experiments to highlight current capabilities and future research challenges.

Bio: Edward Knightly is the Sheafor-Lindsay Professor and Department Chair of Electrical and Computer Engineering and Professor of Computer Science  at Rice University in Houston, Texas. He received his Ph.D. and M.S. from the University of California at Berkeley and his B.S. from Auburn University. He is an ACM Fellow, an IEEE Fellow, and a Sloan Fellow. He received the Dynamic Spectrum Alliance Award for Research on New Opportunities for Dynamic Spectrum Access and the National Science Foundation CAREER Award. He received best paper awards from ACM MobiCom, ACM MobiHoc, IEEE Communications and Network Security (CNS), IEEE SECON(twice), and the IEEE Workshop on Cognitive Radio Architectures for Broadband. He served as general chair or technical chair for ACM MobiHoc, ACM MobiSys, IEEE INFOCOM, and IEEE SECON. He serves as an editor-at-large for IEEE/ACM Transactions on Networking and serves on the IMDEA Networks Scientific Council.

Abstract: As the Internet of Things (IOT) matures and supports increasingly sophisticated applications, the research needs for IOT also expand considerably. This talk discusses several major research challenges for the future IOT where trillions of devices are connected to the Internet; call it the Internet of Trillions of Things (IOTT). Research topics covered include new systems of systems problems, the impact of massive scaling, and IOTT for healthcare. Smart cities are used to present examples of new system of system research issues and their solutions. Scaling and long time maintenance problems give rise to the need for runtime validation. Why this is important and how to accomplish this is presented. We use the Internet of Healthcare Things to identify the realisms that must be addressed in real home deployments. We also discuss the problems and solutions for using speech as a major sensing modality for smart healthcare based on an emo2vec (an extension to word2vec) and LSTMs. The list of topics is not meant to be comprehensive, but does address some of the main research issues in IOTT/CPS.

Bio: Professor John A. Stankovic is the BP America Professor in the Computer Science Department at the University of Virginia and Director of the Link Lab. He served as Chair of the department for 8 years. He is a Fellow of both the IEEE and the ACM. He has been awarded an Honorary Doctorate from the University of York for his work on real-time systems. He won the IEEE Real-Time Systems Technical Committee's Award for Outstanding Technical Contributions and Leadership. He also received the IEEE Technical Committee on Distributed Processing's Distinguished Achievement Award (inaugural winner). He has seven Best Paper awards, including one for ACM SenSys 2006. Stankovic has an h-index of 115 and over 57,000 citations. In 2015 he was awarded the Univ. of Virginia Distinguished Scientist Award, and in 2010 the School of Engineering’s Distinguished Faculty Award. He also received a Distinguished Faculty Award from the University of Massachusetts. He has given more than 40 Keynote talks at conferences and many Distinguished Lectures at major Universities. He also served on the National Academy’s Computer Science Telecommunications Board. He was the Editor-in-Chief for the IEEE Transactions on Distributed and Parallel Systems and was founder and co-editor-in-chief for the Real-Time Systems Journal. His research interests are in real-time systems, wireless sensor networks, smart and connected health, cyber physical systems, and the Internet of Things. Prof. Stankovic received his PhD from Brown University.

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